A new variational principle for the Euclidean distance function: Linear approach to the non-linear eikonal problem

TL;DR

This paper introduces a fast, convolution-based linear approach to approximate the Euclidean distance function, offering advantages over traditional non-linear methods by enabling efficient computation and derivative calculation.

Contribution

The authors develop a linear variational principle that simplifies Euclidean distance computation into a convolution problem, allowing for efficient FFT-based solutions and derivative calculations.

Findings

Converges to the true Euclidean distance as parameter τ approaches zero.

Provides a closed-form convolution solution for the approximate distance function.

Enables efficient computation of derivatives and topological sign using convolutions.

Abstract

We present a fast convolution-based technique for computing an approximate, signed Euclidean distance function $S$ on a set of 2D and 3D grid locations. Instead of solving the non-linear, static Hamilton-Jacobi equation ($\|\nabla S\|=1$), our solution stems from first solving for a scalar field $\phi$ in a linear differential equation and then deriving the solution for $S$ by taking the negative logarithm. In other words, when $S$ and $\phi$ are related by $\phi = \exp \left(-\frac{S}{\tau} \right)$ and $\phi$ satisfies a specific linear differential equation corresponding to the extremum of a variational problem, we obtain the approximate Euclidean distance function $S = -\tau \log(\phi)$ which converges to the true solution in the limit as $\tau \rightarrow 0$. This is in sharp contrast to techniques like the fast marching and fast sweeping methods which directly solve the Hamilton-Jacobi equation by the Godunov upwind discretization scheme. Our linear formulation results in a closed-form solution to the approximate Euclidean distance function expressible as a discrete convolution, and hence efficiently computable using the fast Fourier transform (FFT). Our solution also circumvents the need for spatial discretization of the derivative operator. As $\tau\rightarrow0$ we show the convergence of our results to the true solution and also bound the error for a given value of $\tau$. The differentiability of our solution allows us to compute---using a set of convolutions---the first and second derivatives of the approximate distance function. In order to determine the sign of the distance function (defined to be positive inside a closed region and negative outside), we compute the winding number in 2D and the topological degree in 3D, whose computations can also be performed via fast convolutions. We demonstrate the efficacy of our method through a set of experimental results.